Personalization can be used to improve the experience of both anonymous and known customers. The challenge is that customer data is fragmented in many different systems.

This Guest blog provides insights on what to take into consideration when you want to start working with your customer data.

To learn more about the role of personalization engine and customer data platform and on how they supercharge your customer experience read this article.

Data: the stepping stone towards creating unique and unforgettable customer journeys. This can be achieved through hyper-personalization.

Hyper-personalization means data, analytics, artificial intelligence (AI) and automation are combined to create custom and targeted experiences. Imagine a children’s clothing shop. Personalization can be extended using business-specific data to enhance the overall brand experience. A customer buying clothes for a child between the ages of 3 and 4 will most likely look to buy clothes for ages 4-5 a year later. A platform that helps find what the customer wants, when they want it, will naturally create the impression that the brand cares for them.

In the financial sector, a mobile app of a bank that knows the most executed operations at different times of the day, month, and even different points of the year, and that merges this information with other variables such as geographic location, can mould its landing page to what a specific user is most likely to want to do at that moment in time or the type of service that the user may be interested in.

Data-driven hyper-personalization is dependent on three things: accurate, accessible and integrated data. Without these, companies will struggle to derive insights from their data that can be used to create better customer experiences.

Accuracy is integral to using data to personalize customer journeys. If data are not accurate, few useful insights can be extracted from them, and hence authentic customer profiles cannot be established for use in hyper-personalization projects. Yet guaranteeing accuracy presents a number of hurdles.

A single source of truth can help strengthen data accuracy. It can also secure insights from data that would not necessarily be uncovered if the data were dispersed. It allows for:

Single sources of truth can also be automated, with Cloud hosting generating real-time data from which more valuable insights can be derived. Hosting data off-site can also ensure its security and reduce costs. Using  cloud services providers can mitigate the risk of internal servers going down and the loss of data sets on which the core business depends.

Data is only useful if it is easily understandable by those using it. If data are not presented clearly within an organization, then few insights will be derived from them.

Data is often dispersed, siloed (in a system, process, department, etc.) and isolated, making it hard to find, hard to maintain and impossible to discover correlations.

Sometimes, data is redundant because you can find the same data in multiple systems. Those systems will retrieve different results since they were subject to different assumptions or different business rules.

Clear dashboards should be created using data visualization tools to give all relevant people within the organization quick and easy visibility of key data sets.

Yet accessibility doesn’t just mean how data are visualized, but also the level of access that stakeholders within an organization are given to data sets and the ability to reach consensus about the data’s accuracy. If certain departments do not have the same access to data as others, different perceptions of customers will emerge and any hyper-personalization efforts will become more difficult.

Accessibility should also be opened up to other platforms within and outside an organization. The format in which data are stored should be readable and understandable by external parties – essential in any project where organizations may need to cooperate.

Once the organization’s data are brought together and made accessible, it is key for senior level figures – including the CIO and CEO – to buy into a data-driven approach to personalization. Automation of data processes will allow more time for integrating data into the wider business approach, for instance by redistributing resources to producing data analysis and modelling which help create a better understanding the customer.

Modelling and data analytics combine datasets; refine segments and enrich customer information, such as historical purchase data, social, weather, geographic, demographic and business rules. These are complemented by a cognitive layer which completes a unified view of the customer.

Customer preferences and expectations continue to grow, challenging brands to deliver a customer-centric approach using technology to help create better and more meaningful connections.

Hyper-personalization through the effective use of data improves the connection, satisfaction and loyalty with the right customers at the right time and in the right channel. Yet many organizations are still unable to glean effective insights from data, as opposed to expecting data in itself to deliver answers.

Are you one of them?